Spice Som Guide English

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Spice-SOM Users Guide 2012-02-07 Page 1

SPICE-SOM USERS GUIDE

Cao Thang 2003 2007

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1. INTRODUCTION

This is a users guide for the Spice-SOM - a Self-Organizing Map (SOM) application. It does

not intend to introduce about SOM theories. You can find more knowledge about SOM and

Neural Network (NN) in other textbooks.

Depending on the version, some contents of this material may be different with your

downloaded Spice-SOM.

The purpose of this program is to get you started quickly with Neural Network without having

to go through lengthy theory of the Neural Network background. Once you understand these

programs you will be able to consult the Neural Network materials on a need basis.

Spice-SOM's arm is to introduce NN and SOM to graduated students studying Computational

Intelligence. Currently Spice-SOM has been using by many students around the world. Spice-SOM has interfaces in Vietnamese, English and Japanese.

Spice-SOM was written by CAO THANG (http://www.spice.ci.ritsumei.ac.jp/~thangc/) when

he did researches in the Soft Intelligence Laboratory, Ritsumeikan University, Japan, 2003-

2007.

Spice-SOM and Spice-Neuro can be downloaded at

http://www.spice.ci.ritsumei.ac.jp/~thangc/programs/

If you have questions or requirements about Spice-SOM, please contact the author at"[email protected]". Thank you.


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2. INSTALL THE SPICE-SOM

Download setup file of the Spice-SOM and run setup.exe, setup welcome window will appearson the screen.

Fig. 1. Setup

Select 'Next', and then select a folder into that you want to install Spice-SOM, select 'Next' and

'Next', Spice-SOM will be installed into the selected folder.

Fig. 2. Select Folder


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Note: If the Spice-SOM does not run after installing, you may need to install Microsoft .NET

Framework Redistributable Package 3.5.21022 before installing Spice-SOM.

3. USING SPICE-SOM

Run Spice-SOM by clicking on Spice-SOM icon on your desktop or selecting Start

Programs Cao Thangs Spice-SOM Spice-SOM.

First the program runs with the English interface, you can select Vietnamese or Japanese by

selecting Options Languages.

Fig. 3. Select language

Menu About, README first is a briefly introduction about Spice-SOM and Users

agreements. You should read it carefully before using Spice-SOM.


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Fig. 4. About Spice-SOM


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3.1. Data Preparation

Using your data by Spice-SOM, you should prepare your data by the following format: data is

prepared in text format by rows and columns. The first column is ID, then Inputs and finally

Labels. Values are separated by comma (Comma Separated Value File Format) with CSV filetype, or Tab (Tab Separated Value File Format) with TXT file type. You may use MS Excel to

edit your data, and then save it in text or csv format. For example data with 5 inputs, 4 datasets

is organized as shown in Table 1.

Table 1. Text Data with 5 inputs, 4 dataset

Note: Data should be numeral, except labels. Spice-SOM cannot read your data if there is a

blank data or null data.

There are some good examples in \Data folder of the Spice-SOM:

number5group1dimension is an example with 100 datasets, 1 input.

number5group2dimension is an example with 100 datasets, 2 inputs.

number5group3dimension is an example with 100 datasets, 3 inputs.

ID X1 X2 X3 X4 X5 LABEL

0 0 0 0 0 0 Data 1

1 0 1 1 0 1 Data 2

2 1 0 1 0 1 Data 3

3 1 1 0 1 1 Data 4

ID: ID of Datasets X: Input DataLABEL: Labels of Datasets


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3.2. Load Data

Suppose that we are using the data in number5group3dimension.txt file, 100 datasets and 3

inputs.

Fig. 5. Load Data

In Number of Input and Data Sets, you should select as illustrated in Fig.5. Then, select

command button Browse from TEXT files, the data will be loaded into memory. In DATA

group on the right, you can review each of loaded datasets. If your data is not normalized, you

may use some normalization functions of Spice-SOM.

In Tab Data Visualization, you can see graph of all loaded data if the number of data is not

large, as illustrated in Fig.6.

Bar for

Selecting Data

Normalization Functions

Input Data (Graph)

Input Data

(Table)


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Fig. 6. View Input data

3.3. Network Training

Fig. 7. Select learning parameters


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2.3.1. Select Network and Training Parameters

The training interface of Spice-SOM is shown in Fig.7. Before train your SOM network, you

should choose its parameters as follows:

Sigma Max: Maximum value of neighborhood function of a winner neuron.

Sigma Min: Minimum value of neighborhood function of a winner neuron.

Sigma Decreasing Rate: Decreasing rate of value of neighborhood function after one

iteration

Learning Rate Max: Maximum value of learning rate

Learning Rate Min: Minimum value of learning rate

Iterations: Number of iterations or epochs

Inhibition: Inhibition value of Mexican Hat neighborhood function

X Neurons:Number of Neurons in x directions

Y Neurons: Number of Neurons in y directions

If you select Continue current weights, the network will learn without resetting its initial

weights. If you select View Graph Online, Graphs of Neighborhood Function and Mean of

Square Error (MSE) will be shown on learning process, however the network will learn more

slowly because your computer have to draw the graphs together with to train the network.

Neighborhood Functions: You should select a type of neighborhood functions. Spice-SOM gives

you some standard neighborhood functions as Rectangular, Gaussian, Mexican and Linear hat

functions.

Select Topology: Default topology of Spice-SOM is Rectangular. You can select Hexagonal

topology by Checkbox Hexagonal Topology. Distances of neurons in these two topologies are

different as illustrated in Fig.8.

HEXAGONAL:O O O O O O O O O

O O O & & & O O OO O & @ @ & O O O

O O & @ + @ & O O

O O & @ @ & O O O

O O O & & & O O O

O O O O O O O O O

RECTANGULAR:O O O O O O O O O

O O O O & O O O OO O O & @ & O O O

O O & @ + @ & O O

O O O & @ & O O O

O O O O & O O O O

O O O O O O O O O

Fig. 8. Rectangular Topology and Hexagonal Topology


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2.3.2. Training

After selecting suitable parameters, you can train the network. Here are main command buttons:

Training: Start the training process.

Save Weights to Binary File: Save current network weights to binary file.

Save Weights to Text File: Save current network weights to text file, you can easily

check each neuron's weights in this text file.

Load Weights from Binary File: Load weights from a saved binary file.

Save MSE Graph to Text File: Save MSE graph data to text file.

Set Default Value: Set parameters as their default values.

Table 2 illustrates MSE graph data that is saved in a text file. Table 3 illustrates network weights

that are saved on a text file.

Table 2. MSE graph data

Iteration Error Neighbor Distance Learning Rate

0 10.02597948 10 0.0991

1 4.14776422 9.9 0.0982

2 3.42406203 9.8 0.0973

3 3.378296441 9.7 0.0964

4 3.256567794 9.6 0.0955

5 3.209400383 9.5 0.0946

95 1.018524437 2 0.0136

96 1.011947207 2 0.0127

97 1.007276918 2 0.0118

98 1.002205101 2 0.0109

99 0.998116728 2 0.0100


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Table 3. Network weights in text file

Neurons Input0 Input1 Input2

Neuron Y = 0, X = 0: 36.99527651 40.76692789 37.21308

Neuron Y = 0, X = 1: 36.85168189 40.01821143 36.97461

Neuron Y = 0, X = 2: 36.36203974 39.09389637 36.61739

Neuron Y = 0, X = 3: 35.27379557 38.58864861 36.22565

Neuron Y = 19, X = 16: 19.76819369 5.711848278 3.108250741

Neuron Y = 19, X = 17: 17.5739719 6.150696979 3.292584748

Neuron Y = 19, X = 18: 12.60898022 6.978729306 3.15416311

Neuron Y = 19, X = 19: 8.678481001 7.590027624 2.853186784

4. Data Distribution Map

After the network learning, you can see the data distribution map in Tab Output Distribution

Image as illustrated in Fig.9. On the map, neurons are arranged in rows and columns. Click

mouse on a neuron position, in the detailed neuron window you will see labels of data that are

fallen on this neuron. You can also see distances from selected neuron (by clicking left mouse

button) to its neighbor neurons.

You can display blank neuron color, winner neuron color, or distance color between neurons.

The distance color between two neurons is distance between them displayed by grayscale with

value 0 (farthest) and 255 (closest). Color of a neuron is an average of distance colors from this

neuron to its adjacent neurons. Gray color between two neurons presents the distance color

between them.

The options for displaying distribution map are the following.

View detailed on left bottom: Put the detailed window on the left bottom

View detailed on right top: Put the detailed window on the top right

Keep Previous Connections: Keep current connections while viewing new connections

from new selected neurons

Distance Color: Select gray level for distance color.

Distance Line: Select value level for displaying connections on the map.

Map Scale: Select scale of the map on the screen.


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Show Output Map Options: Display options for distribution map:

o Image Width, Image Height: Size of the map that you want to save (in case you

want to resize the map).

o Image Format: Format of the saved map (JPG, PNG, BMP)

o Print Neuron Number: Print orders of neurons on the map

o Fill Blank Neuron: Print distance color of blank neurons.

o Fill Winner Neuron: Print distance color of winner neurons.

o Show Distance Color: Print distance colors between neurons.

o Save Original Size: Save the map with original size.

o Show Hexagonal Topology: Select hexagonal topology for the map (default is

rectangular topology).

Select the command button Save Output Image, the output map will be saved on hard disk.

Figs.10 and 11 show an example of output map.

You can view the output table by selecting the Tab Output Distribution Table, as illustrated in

Fig.12. You also can save this table by command button Save Output Table. After re-training

the network, you need to choose command button Refresh Output Table.


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Fig 10. Output map (hexagonal topology)

Fig. 9. Output map after learning

Detailed of

selected neuron

Distance from a neuron to its

neighbor neurons

Options for output

map

Gray color on a neuron

demonstrates its distances to

adjency neurons

Gray color between two neurons

demonstrates their own distances

Selecting bars for distance color and

distance line values

Save current output map


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Fig 11. Output map (Rectangular topology)

Fig 12. Output Table


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5. Conclusions

This material guides you to use Spice-SOM, a Self-Organizing Map application. Having used

this application, you may have a better understanding on Self-Organizing Map. Like Spice-

Neuro (a Multi-Layer Neural Network Application), you can use Spice-SOM to model various

data in different practical domains such as pattern recognition, clustering, decision making... The

author hopes that Spice-SOM would be useful for your study and research.

Thank you for using Spice-SOM. If you need more functions in Spice-SOM, please do not

hesitate to contact author at [email protected]. Your ideas and requirements are

always welcomed.

Thank you!

Documents

Spice Som Guide English